1. Field
Embodiments of the present invention relate to computer graphics and particularly to three dimensional shape matching and retrieval.
2. Background Art
Laser-scanned objects, CAD models, and even image based reconstructions are a few of the sources contributing to the rapidly growing number of publicly available 3D model collections. Along with these 3D model collections, which are often vast, comes the need for fast, large-scale model matching and retrieval.
3D model retrieval continues to be an important practical as well as fundamental problem. At the core of any content-based model retrieval engine lies the challenge of computing 3D shape similarity. From the practical perspective, many large web repositories of 3D content fail to leverage shape content for model search, which often leads to search results of limited success and applicability.
One of the challenges for 3D shape matching is the wide variety of transformations that must be accounted for when comparing 3D models. For example, the most common transformations to which any 3D retrieval engine must be invariant are global changes in size, position, and orientation or 3D rotation.
In this regard, most of the approaches can be divided into two categories. The first category contains those approaches where invariance to possible transformations is built directly into extracted descriptors which are used to represent the 3D model. The second category contains those approaches that address the possible transformations of a model at the time when 3D model descriptors are being compared. However, there is a disadvantage when 3D model descriptors are compared because all possible transformations of the 3D model must be accounted for, leading to an increase in the cost of comparison.